@article{ART003139173},
author={Hyeon-Jae Kwon and Sangmin Suh},
title={Effective Automatic Weed Detection With Improved YOLOv10},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2024},
volume={29},
number={11},
pages={89-96},
doi={10.9708/jksci.2024.29.11.089}
TY - JOUR
AU - Hyeon-Jae Kwon
AU - Sangmin Suh
TI - Effective Automatic Weed Detection With Improved YOLOv10
JO - Journal of The Korea Society of Computer and Information
PY - 2024
VL - 29
IS - 11
PB - The Korean Society Of Computer And Information
SP - 89
EP - 96
SN - 1598-849X
AB - In this paper, we design an improved weed detection model using YOLOv10, a deep learning-based object detection algorithm. YOLOv10 improves its performance compared to previous versions by adding an attention module, the PSA module. PSA is strong at recognising complex patterns in large areas because it uses some features of its own attention to reduce computation and learn global information. However, it may be inefficient for certain problems, such as weeds, which are generally small objects. Therefore, in this paper, we propose an improved YOLOv10 by applying another attention module, SENet, instead of the PSA module. Since, SENet learns the importance between channels, it can learn the features of weeds in more detail than the PSA module. In addition, SENet is lighter, less computationally intensive, and faster than the PSA module, so we conducted experiments by replacing the PSA module with SENet, which is suitable for weed detection. The experiment consisted of 200 training runs with a total of 14 classes, and we compared the performance through various performance evaluations. The experimental results showed that the FPS increased from 476.19 to 526.32, which is about 9.52% processing speed improvement. The mAP50-95 value increased from 88.7% to 88.3%, which shows that the proposed model is lighter than the existing model and performs similarly to the existing model.
KW - Weed;YOLOv10;Detection;Deep learning;Agriculture
DO - 10.9708/jksci.2024.29.11.089
ER -
Hyeon-Jae Kwon and Sangmin Suh. (2024). Effective Automatic Weed Detection With Improved YOLOv10. Journal of The Korea Society of Computer and Information, 29(11), 89-96.
Hyeon-Jae Kwon and Sangmin Suh. 2024, "Effective Automatic Weed Detection With Improved YOLOv10", Journal of The Korea Society of Computer and Information, vol.29, no.11 pp.89-96. Available from: doi:10.9708/jksci.2024.29.11.089
Hyeon-Jae Kwon, Sangmin Suh "Effective Automatic Weed Detection With Improved YOLOv10" Journal of The Korea Society of Computer and Information 29.11 pp.89-96 (2024) : 89.
Hyeon-Jae Kwon, Sangmin Suh. Effective Automatic Weed Detection With Improved YOLOv10. 2024; 29(11), 89-96. Available from: doi:10.9708/jksci.2024.29.11.089
Hyeon-Jae Kwon and Sangmin Suh. "Effective Automatic Weed Detection With Improved YOLOv10" Journal of The Korea Society of Computer and Information 29, no.11 (2024) : 89-96.doi: 10.9708/jksci.2024.29.11.089
Hyeon-Jae Kwon; Sangmin Suh. Effective Automatic Weed Detection With Improved YOLOv10. Journal of The Korea Society of Computer and Information, 29(11), 89-96. doi: 10.9708/jksci.2024.29.11.089
Hyeon-Jae Kwon; Sangmin Suh. Effective Automatic Weed Detection With Improved YOLOv10. Journal of The Korea Society of Computer and Information. 2024; 29(11) 89-96. doi: 10.9708/jksci.2024.29.11.089
Hyeon-Jae Kwon, Sangmin Suh. Effective Automatic Weed Detection With Improved YOLOv10. 2024; 29(11), 89-96. Available from: doi:10.9708/jksci.2024.29.11.089
Hyeon-Jae Kwon and Sangmin Suh. "Effective Automatic Weed Detection With Improved YOLOv10" Journal of The Korea Society of Computer and Information 29, no.11 (2024) : 89-96.doi: 10.9708/jksci.2024.29.11.089
